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Not Falling for the Details: Metacognition May Make Us More Immune to Popular Falsehoods

03 Jan 2018 10:00 AM | Anonymous

By Teresa Ober, Ph.D. Candidate, The Graduate Center CUNY

Why are we prone to make mistakes in light of misleading information, even when accurate information is right there in front of us?

Demonstration #1

Before we get started on addressing this question, I would like to you to try to answer another very straightforward question: Are there more words in the English language that begin with the letter r or have r as their third letter?

The answer, as some, though likely not all, may have guessed (without reading ahead, of course!) was that there are more words that have r as the third letter. In reality, there are nearly twice as many words that have the letter r in the third position as opposed to the first position by some estimates. Most people guess that there are more words that begin with r because such words are easier to generate; however, there are apparently many more words that have r as the third letter (see Tversky and Kahneman, 1973). To name a few as examples: car, bird, warm, xerox, etc.

But why does this seem so counterintuitive? Probably because words that begin with the letter r tend to be more familiar to us than words that have r as the third letter. Simply put, knowledge of words that begin with the letter r are more available to the mind. This is a common phenomenon generally referred to as the availability heuristic.

Demonstration #2

It is likely that more than several of our own students will use the availability heuristic to judge the frequency or likelihood of certain occurrences. In the 1970’s, Slovic, Fischhoff, and Lichtenstein (1976) discovered that only a small percentage of participants, who happened to be college students at the time, gave the correct answers when asked which of two options was more likely to lead to a fatality. A pairing included, for example, fatality due to a tornado vs. asthma. The odds ratio of a fatality due to a tornado relative to asthma is 1 to 20.90, meaning that for every 1 fatality due to a tornado, there are approximately 20.90 fatalities due to asthma. Based on these figures, asthma is on average deadlier than a tornado. When presented with the odds ratios and probabilities, the answers may seem obvious, but as the authors of this original study found out, participants were more often incorrect than not. Of those who responded to the questions, only 42% guessed that asthma was more fatal than a tornado.

Has increased media reporting on some of these causes of death (e.g., asthma) changed estimates since this study was conducted first in 1976? Perhaps. Has our media awareness actually improved across the board? Most likely not.

In judging the frequency of fatalities from different causes (morbid, I know), people tend to overestimate the number of deaths from, say, tornados, but underestimate the number of fatalities from, say, asthma, despite the latter once having been much more common. This is because we are more likely hear about the dangers of tornados sensationalized in the news, but we are much less likely to recognize the physical and health risks of asthma.

This demonstration also provides a teachable moment for students by demonstrating to them that in today’s world, we should be especially conscious of the availability heuristic when making judgments about things we hear, regardless of whether the source is the news, social media, family or friends, etc. While there is a convenience of choosing types of news and news sources that you have readily presented to you at your leisure, one could argue that it allows people to construct a siloed version of current events. This could be problematic if news you choose is not always accurate and honest.

We use the availability heuristic when we estimate frequency or probability in terms of how easily we can think of examples of something. This heuristic is generally accurate in our daily lives, and people can estimate relative frequency with impressive accuracy. However, this type of availability comes with the cost that it may be potentially contaminated by two factors that are not related to objective frequency: recency and familiarity. Therefore, when you make judgments about the frequency or likelihood of something happening, consider asking yourself whether you are giving an advantage to items that occurred more recently or that are somehow more familiar.

The use of the availability heuristic is so pervasive that instructors and students alike may not even notice that we have succumbed to using it at our convenience. The availability heuristic may lead us to make illusory correlations, which occur when two variables appear to be correlated, although there is actually no statistical relationship. For example:

The weather is always bad on the weekend.

The bus/train is always late when you are running behind.

The phone always rings when you are busy.

But how can instructors get students to be more conscious of the negative consequences of the availability heuristic, if we ourselves are susceptible to it? One way is to get them to consciously strive to observe true frequencies. By doing so, instructors may encourage students to use their own metacognition to separate true relationships from merely perceived ones.

Preventing this phenomenon can be done simply by calculating an odds ratio, but most people don’t bother to do this (or don’t know how to do so). If students did, they may become more aware of the types of inaccurate illusory correlations that are salient and difficult to reason through due to an overactive availability heuristic.

In-Class Activity #1

Here is a quick activity to get students thinking about likelihood by learning about odds ratios (adapted from Prasad et al., 2008).

  1. First, determine the type of sport the students might be interested in. For the purpose of this example, let’s say it’s basketball.
  2. Then ask the students: what do people mean when they say—the odds of your favorite basketball team winning a game is 1:1? Some students would say that the favorite team has the same chance of winning as they do losing. Others might reply that it means your team has a 1 out of 2 or 50% chance of winning this game. Both answers are correct.
  3. You can explain that odds correlate to probability. For example, a 1:3 odds indicates that your favorite team is expected to win 1 in every 4 attempts, hence the probability is 25%
  4. Now test students’ understanding on new odds ratios. For example, a 4:1 corresponding to an 80% chance because 4/(4+1) = 80%, and 1:5, corresponding to a 20% chance because 1/(1+4) = 20%, and so forth.
  5. Inform students that odds ratios are not just useful in shattering expectations formed from illusory correlations, but have actually been used in the medical sciences for many years. (It is necessary to understand relative risk, that is, how likely someone is going to have a certain condition based on some piece of information you have about them—something that can also be introduced with an example, such as the relative risk of developing lung cancer if you smoke.)

In-Class Activity #2

Next, provide another hypothetical example based on the contingency table below. 

Train Late

Train Not Late

Odds Ratio

(Train Late: Not Late)


(Train late)

Running Behind





Not Running Behind





In this hypothetical example, inform students that you decided to keep of the number of times the train (or bus) arrived late when you were running behind. Without showing them the odds ratio just yet, ask them to speculate whether there was a relationship between running behind (or not) and the train arriving on time (or not). In fact, there would be no relationship based on the figures in this table. The odds of a train running late when you are or are not running behind is exactly the same. The odds ratio is 2 to 3, or a 40% probability which is 2/(2+3). That means that the probability of the training running behind is actually 40% in this example, regardless of whether you are running late or not. 

Take-Home Activity

Next, students in groups choose a perceived correlation and set out to record it using a contingency table, such as the one shown in the previous example.

During the next class, students briefly report their findings.

In so doing, they discuss whether the perceived relationship be due to an actual correlation?

If so, what might be the relationship between variables? Is the perceived relationship may be due to contaminants in the availability heuristic? If so, was it due to either recency or familiarity and what do they take as evidence of this?

While this may seem simple enough, getting students to stop and think about the information available to them in their environment and apply metacognition without jumping too quickly to rash conclusions may provide a powerful lifelong cognitive tool. These simple classroom demonstrations and activities of a popular phenomenon from cognitive psychology may help students understand an essential concept while also preparing them to think more critically about the world around them.


Hamilton, D. L., & Gifford, R. K. (1976). Illusory correlation in interpersonal perception: A cognitive basis of stereotypic judgments. Journal of Experimental Social Psychology, 12(4), 392-407.

Prasad, K., Jaeschke, R., Wyer, P., Keitz, S., & Guyatt, G. (2008). Tips for teachers of evidence-based medicine: understanding odds ratios and their relationship to risk ratios. Journal of General Internal Medicine, 23(5), 635-640.

Slovic, P., Fischhoff, B., & Lichtenstein, S. (1976). Cognitive processes and societal risk taking. In J. S. Carroll & J. W. Payne (Eds.), Cognition and Social Behavior. Hillsdale, NJ: Erlbaum.

Tversky, A., & Kahneman, D. (1973). Availability: A heuristic for judging frequency and probability. Cognitive Psychology, 5, 207-232.

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